A Unified Calibration Framework for High-Accuracy Articulated Robot Kinematics

TL;DR

This paper introduces a unified static calibration method for industrial robots that models geometric and non-geometric errors with a single experiment, significantly improving accuracy and robustness.

Contribution

It presents a novel unified calibration framework that incorporates multiple error sources into a single model using virtual joints and Gauss-Newton optimization.

Findings

Achieves a mean position error of 26.8 μm on a KUKA KR30 robot.

Model is robust and well-conditioned according to Fisher information spectra.

Outperforms purely geometric calibration with a lower error of 102.3 μm.

Abstract

Researchers have identified various sources of tool positioning errors for articulated industrial robots and have proposed dedicated compensation strategies. However, these typically require individual, specialized experiments with separate models and identification procedures. This article presents a unified approach to the static calibration of industrial robots that identifies a robot model, including geometric and non-geometric effects (compliant bending, thermal deformation, gear transmission errors), using only a single, straightforward experiment for data collection. The model augments the kinematic chain with virtual joints for each modeled effect and realizes the identification using Gauss-Newton optimization with analytic gradients. Fisher information spectra show that the estimation is well-conditioned and the parameterization near-minimal, whereas systematic temporal cross-validation and model ablations demonstrate robustness of the model identification. The resulting model is very accurate and its identification robust, achieving a mean position error of 26.8 $\mu m$ on a KUKA KR30 industrial robot compared to 102.3 $\mu m$ for purely geometric calibration.